This script is Part 2 of the NICHES workflow. In this script, we will
# Require packages
require(Seurat)
require(NICHES)
require(ggplot2)
require(cowplot)
require(dplyr)
load("~/Library/CloudStorage/GoogleDrive-michasam.raredon@yale.edu/.shortcut-targets-by-id/1VLqBlyzO-Qad5O2kbwXkBRh_1cQho39t/Engineered Lung Paper/Global_Connectomics/global.connectomics.2023-11-11.Robj")
p1 <- VlnPlot(global.connectomics$CellToCell$RNA,group.by='orig.ident',c('nFeature_CellToCell'),raster=F,pt.size = 0.1)+ggtitle('Not Imputed')+ylab('nFeature_CellToCell')
p2 <- VlnPlot(global.connectomics$CellToCell$alra,group.by='orig.ident',c('nFeature_CellToCell'),raster=F,pt.size = 0.1)+ggtitle('Imputed')+ylab('nFeature_CellToCell')
png(filename = 'CellToCell_BeforeFiltration.png',width = 12,height = 7,units = 'in',res=300)
plot_grid(p1,p2,nrow=1)
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,nrow=1)
Usually we would like to filter at >50 features for Cell-To-Cell and >100 features for the System metrics. But that will cut out basically the entirety of some of the lower information depth samples. The low infomation depth we see in BCL5, BC1P3 and BC1P6 could be a consequence of their not being sequenced as deeply as the others, or it could be biologic. We don’t know yet.
p1 <- VlnPlot(global.connectomics$SystemToCell$RNA,group.by='orig.ident',c('nFeature_SystemToCell'),raster=F,pt.size = 0.1)+ggtitle('Not Imputed')+ylab('nFeature_SystemToCell')
p2 <- VlnPlot(global.connectomics$SystemToCell$alra,group.by='orig.ident',c('nFeature_SystemToCell'),raster=F,pt.size = 0.1)+ggtitle('Imputed')+ylab('nFeature_SystemToCell')
png(filename = 'SystemToCell_BeforeFiltration.png',width = 12,height = 7,units = 'in',res=300)
plot_grid(p1,p2,nrow=1)
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,nrow=1)
Same comment here, though here it is even clearer. BCL5 is a serious outlier, with very low information depth. Is this because of sequnecing, or biology? Not fully sure.
The fact that all the autocrine populations are lower here, however, is almost certainly because the lower cellular idveristy is reducing the feature number of individual System level measurements.
p1 <- VlnPlot(global.connectomics$CellToSystem$RNA,group.by='orig.ident',c('nFeature_CellToSystem'),raster=F,pt.size = 0.1)+ggtitle('Not Imputed')+ylab('nFeature_CellToSystem')
p2 <- VlnPlot(global.connectomics$CellToSystem$alra,group.by='orig.ident',c('nFeature_CellToSystem'),raster=F,pt.size = 0.1)+ggtitle('Imputed')+ylab('nFeature_CellToSystem')
png(filename = 'CellToSystem_BeforeFiltration.png',width = 12,height = 7,units = 'in',res=300)
plot_grid(p1,p2,nrow=1)
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,nrow=1)
So, now we need to choose reasonable cleaning thresholds. Very tricky for this specific dataset. We need to be extraordinarily conservative or we risk losing key samples.
We might be forced, because of this, to use the imputed data. But for now, let’s try as a first pass / demonstration:
global.connectomics$CellToCell$RNA <- subset(global.connectomics$CellToCell$RNA,nFeature_CellToCell>25)
global.connectomics$CellToCell$alra <- subset(global.connectomics$CellToCell$alra,nFeature_CellToCell>50)
global.connectomics$SystemToCell$RNA <- subset(global.connectomics$SystemToCell$RNA,nFeature_SystemToCell>50)
global.connectomics$SystemToCell$alra <- subset(global.connectomics$SystemToCell$alra,nFeature_SystemToCell>50)
global.connectomics$CellToSystem$RNA <- subset(global.connectomics$CellToSystem$RNA,nFeature_CellToSystem>50)
global.connectomics$CellToSystem$alra <- subset(global.connectomics$CellToSystem$alra,nFeature_CellToSystem>50)
Note that these thresholds are lower than what would be my go-to for most, native tissue datasets. This engineered dataset requires elevated caution at this computational step, so I am reducing the above thresholds as low as I think is reasonable to get data.
p1 <- VlnPlot(global.connectomics$CellToCell$RNA,group.by='orig.ident',c('nFeature_CellToCell'),raster=F,pt.size = 0.1)+ggtitle('Not Imputed')+ylab('nFeature_CellToCell')
p2 <- VlnPlot(global.connectomics$CellToCell$alra,group.by='orig.ident',c('nFeature_CellToCell'),raster=F,pt.size = 0.1)+ggtitle('Imputed')+ylab('nFeature_CellToCell')
png(filename = 'CellToCell_AfterFiltration.png',width = 12,height = 7,units = 'in',res=300)
plot_grid(p1,p2,nrow=1)
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,nrow=1)
p1 <- VlnPlot(global.connectomics$SystemToCell$RNA,group.by='orig.ident',c('nFeature_SystemToCell'),raster=F,pt.size = 0.1)+ggtitle('Not Imputed')+ylab('nFeature_SystemToCell')
p2 <- VlnPlot(global.connectomics$SystemToCell$alra,group.by='orig.ident',c('nFeature_SystemToCell'),raster=F,pt.size = 0.1)+ggtitle('Imputed')+ylab('nFeature_SystemToCell')
png(filename = 'SystemToCell_AfterFiltration.png',width = 12,height = 7,units = 'in',res=300)
plot_grid(p1,p2,nrow=1)
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,nrow=1)
p1 <- VlnPlot(global.connectomics$CellToSystem$RNA,group.by='orig.ident',c('nFeature_CellToSystem'),raster=F,pt.size = 0.1)+ggtitle('Not Imputed')+ylab('nFeature_CellToSystem')
p2 <- VlnPlot(global.connectomics$CellToSystem$alra,group.by='orig.ident',c('nFeature_CellToSystem'),raster=F,pt.size = 0.1)+ggtitle('Imputed')+ylab('nFeature_CellToSystem')
png(filename = 'CellToSystem_AfterFiltration.png',width = 12,height = 7,units = 'in',res=300)
plot_grid(p1,p2,nrow=1)
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,nrow=1)
But ya know, the above workflows steps still make me uncomfortable. What about the BASCs, which we know are basically only in the BCL5 condition, and we know have an extraordinary low feature depth? I think we need to be careful here with filtration, or run the risk of carving out the BASC connectivity before we even have had a chnace to look at it.
SO, let’s start over, and not filter any of the NICHES data at all prior to initial visualization. If we want to filter/clean later, we can do so.
load("~/Library/CloudStorage/GoogleDrive-michasam.raredon@yale.edu/.shortcut-targets-by-id/1VLqBlyzO-Qad5O2kbwXkBRh_1cQho39t/Engineered Lung Paper/Global_Connectomics/global.connectomics.2023-10-27.Robj")
# Scale
global.connectomics$CellToCell$RNA <- ScaleData(global.connectomics$CellToCell$RNA)
global.connectomics$CellToCell$alra <- ScaleData(global.connectomics$CellToCell$alra)
global.connectomics$SystemToCell$RNA <- ScaleData(global.connectomics$SystemToCell$RNA)
global.connectomics$SystemToCell$alra <- ScaleData(global.connectomics$SystemToCell$alra)
global.connectomics$CellToSystem$RNA <- ScaleData(global.connectomics$CellToSystem$RNA)
global.connectomics$CellToSystem$alra <- ScaleData(global.connectomics$CellToSystem$alra)
global.connectomics$CellToCell$RNA <- FindVariableFeatures(global.connectomics$CellToCell$RNA)
global.connectomics$CellToCell$alra <- FindVariableFeatures(global.connectomics$CellToCell$alra)
global.connectomics$SystemToCell$RNA <- FindVariableFeatures(global.connectomics$SystemToCell$RNA)
global.connectomics$SystemToCell$alra <- FindVariableFeatures(global.connectomics$SystemToCell$alra)
global.connectomics$CellToSystem$RNA <- FindVariableFeatures(global.connectomics$CellToSystem$RNA)
global.connectomics$CellToSystem$alra <- FindVariableFeatures(global.connectomics$CellToSystem$alra)
global.connectomics$CellToCell$RNA <- RunPCA(global.connectomics$CellToCell$RNA,npcs = 100)
global.connectomics$CellToCell$alra <- RunPCA(global.connectomics$CellToCell$alra,npcs = 100)
global.connectomics$SystemToCell$RNA <- RunPCA(global.connectomics$SystemToCell$RNA,npcs = 100)
global.connectomics$SystemToCell$alra <- RunPCA(global.connectomics$SystemToCell$alra,npcs = 100)
global.connectomics$CellToSystem$RNA <- RunPCA(global.connectomics$CellToSystem$RNA,npcs = 100)
global.connectomics$CellToSystem$alra <- RunPCA(global.connectomics$CellToSystem$alra,npcs = 100)
pdf(file='global.connectomics$CellToCell$RNA.PCs.pdf',width=10,height=8)
ElbowPlot(global.connectomics$CellToCell$RNA,ndims = 100)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=1:9)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=10:18)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=19:27)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=28:36)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=37:45)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=46:54)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=55:63)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=64:72)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=73:81)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=82:90)
PCHeatmap(global.connectomics$CellToCell$RNA,cells=200,balanced=T,dims=91:99)
dev.off()
## quartz_off_screen
## 2
pdf(file='global.connectomics$CellToCell$alra.PCs.pdf',width=10,height=8)
ElbowPlot(global.connectomics$CellToCell$alra,ndims = 100)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=1:9)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=10:18)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=19:27)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=28:36)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=37:45)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=46:54)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=55:63)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=64:72)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=73:81)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=82:90)
PCHeatmap(global.connectomics$CellToCell$alra,cells=200,balanced=T,dims=91:99)
dev.off()
## quartz_off_screen
## 2
pdf(file='global.connectomics$SystemToCell$RNA.PCs.pdf',width=10,height=8)
ElbowPlot(global.connectomics$SystemToCell$RNA,ndims = 100)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=1:9)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=10:18)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=19:27)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=28:36)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=37:45)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=46:54)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=55:63)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=64:72)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=73:81)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=82:90)
PCHeatmap(global.connectomics$SystemToCell$RNA,cells=200,balanced=T,dims=91:99)
dev.off()
## quartz_off_screen
## 2
pdf(file='global.connectomics$SystemToCell$alra.PCs.pdf',width=10,height=8)
ElbowPlot(global.connectomics$SystemToCell$alra,ndims = 100)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=1:9)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=10:18)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=19:27)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=28:36)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=37:45)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=46:54)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=55:63)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=64:72)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=73:81)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=82:90)
PCHeatmap(global.connectomics$SystemToCell$alra,cells=200,balanced=T,dims=91:99)
dev.off()
## quartz_off_screen
## 2
pdf(file='global.connectomics$CellToSystem$RNA.PCs.pdf',width=10,height=8)
ElbowPlot(global.connectomics$CellToSystem$RNA,ndims = 100)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=1:9)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=10:18)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=19:27)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=28:36)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=37:45)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=46:54)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=55:63)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=64:72)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=73:81)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=82:90)
PCHeatmap(global.connectomics$CellToSystem$RNA,cells=200,balanced=T,dims=91:99)
dev.off()
## quartz_off_screen
## 2
pdf(file='global.connectomics$CellToSystem$alra.PCs.pdf',width=10,height=8)
ElbowPlot(global.connectomics$CellToSystem$alra,ndims = 100)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=1:9)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=10:18)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=19:27)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=28:36)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=37:45)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=46:54)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=55:63)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=64:72)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=73:81)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=82:90)
PCHeatmap(global.connectomics$CellToSystem$alra,cells=200,balanced=T,dims=91:99)
dev.off()
## quartz_off_screen
## 2
global.connectomics$CellToCell$RNA <- RunUMAP(global.connectomics$CellToCell$RNA,dims = 1:75)
global.connectomics$CellToCell$alra <- RunUMAP(global.connectomics$CellToCell$alra,dims = 1:75)
global.connectomics$SystemToCell$RNA <- RunUMAP(global.connectomics$SystemToCell$RNA,dims = 1:75)
global.connectomics$SystemToCell$alra <- RunUMAP(global.connectomics$SystemToCell$alra,dims = 1:75)
global.connectomics$CellToSystem$RNA <- RunUMAP(global.connectomics$CellToSystem$RNA,dims = 1:75)
global.connectomics$CellToSystem$alra <- RunUMAP(global.connectomics$CellToSystem$alra,dims = 1:75)
load("~/Library/CloudStorage/GoogleDrive-michasam.raredon@yale.edu/.shortcut-targets-by-id/1VLqBlyzO-Qad5O2kbwXkBRh_1cQho39t/Engineered Lung Paper/Allie's Object Filtration/Final Objects/all.color_palettes_2.R")
color_pals
## $sample_colors
## BC1P3 BC1P6 RLMVEC FB13 FB14 BAL BCL5 BCEC2
## "#9E0142" "#B91F48" "#D53E4F" "#E45549" "#F46D43" "#FDAE61" "#FEE08B" "#FEEFA4"
## BEF1 BEF2 BEF3 BEF12 BEF14 BEF15 BEFM1 BEFM2
## "#ABDDA4" "#88CFA4" "#66C2A5" "#46B8DA" "#40A6D1" "#3B95C8" "#08519C" "#08306B"
## BEFM4 BEFM5 BEFM6 TXP3_L TXP4_L
## "#AD8FB8" "#A367B8" "#993FB8" "#BDBDBD" "#737373"
##
## $dataset_colors
## Start Mono Co Tri_E Tri_L Quad_E Quad_L TXP_L
## "#B91F48" "#F88D51" "#FEEFA4" "#88CFA4" "#40A6D1" "#08306B" "#993FB8" "#BDBDBD"
##
## $class_colors
## Epithelium Endothelium Mesenchyme Immune
## "#75C359" "#5B9ADA" "#E64955" "#8949CA"
##
## $niche_colors
## Start Eng Native
## "#FDAE61" "#08306B" "darkgrey"
##
## $starteng_colors
## Start Eng
## "#FDAE61" "#08306B"
##
## $epi
## Basal-like ATI-like Transitional Cycling BASC
## "#66C2A5" "#FC8D62" "#8DA0CB" "#E78AC3" "#A6D854"
##
## $endo
## Microvascular Cycling_Microvascular Lymphatic
## "#66C2A5" "#FC8D62" "#8DA0CB"
## Cycling_Lymphatic Progenitor
## "#E78AC3" "#A6D854"
##
## $mes
## Alveolar Cycling Remodeling Aberrant Pericytes
## "#66C2A5" "#FC8D62" "#E78AC3" "#8DA0CB" "#A6D854"
##
## $imm
## Alveolar Cycling Interstitial
## "#66C2A5" "#8DA0CB" "#FC8D62"
##
## $imm2
## B ILC Mac_Alv Mac_Alv_Pro Mac_Int Mo_Act
## "#7570B3" "#B3B3B3" "#66C2A5" "#8DA0CB" "#FC8D62" "#FFD92F"
## Mo_NC Neutro Plasma T
## "#E7298A" "#E78AC3" "#A6D854" "#66A61E"
color_pals$type_colors <- c(color_pals$epi,color_pals$endo,color_pals$mes,color_pals$imm)
names(color_pals$type_colors) <-c("Basal-like","ATI-like","Transitional","Cycling_Epi","BASC",
"Microvascular","Cycling_Microvascular","Lymphatic","Cycling_Lymphatic","Endo_Progenitor",
"Alveolar_Fibroblast","Cycling_Mes","Remodeling_Fibroblast","Aberrant_Fibroblast","Pericytes",
"Alveolar_Macrophage","Cycling_Alveolar_Macrophage","Interstitial_Macrophage")
color_pals$type_colors <- c(color_pals$type_colors,'blue')
names(color_pals$type_colors) <-c("Basal-like","ATI-like","Transitional","Cycling_Epi","BASC",
"Microvascular","Cycling_Microvascular","Lymphatic","Cycling_Lymphatic","Endo_Progenitor",
"Alveolar_Fibroblast","Cycling_Mes","Remodeling_Fibroblast","Aberrant_Fibroblast","Pericytes",
"Alveolar_Macrophage_Naive","Cycling_Alveolar_Macrophage","Interstitial_Macrophage",'Alveolar_Macrophage_Activated')
#### ABOVE DOES NOT WORK, NEEDS ADJUSTMENT
## For now:
color_pals$type_colors <- c('#A40606','#9CFFFA','#B0DB43','#9C528B','#2F6690',
'#946846','#F1C40F','green','#0F0326','#E65F5C','#14591D','#726DA8',
'yellow','purple','blue','red','orange','darkgrey','magenta')
names(color_pals$type_colors) <- unique(global.connectomics$CellToCell$alra$SendingType)
p1 <- DimPlot(global.connectomics$CellToCell$RNA,group.by = 'Dataset2.Sending',cols=color_pals$dataset_colors,raster=F,shuffle = T)
p2 <- DimPlot(global.connectomics$CellToCell$RNA,group.by = 'orig.ident',cols = color_pals$sample_colors, raster=F,shuffle = T)
p3 <- DimPlot(global.connectomics$CellToCell$RNA,group.by = 'class.Sending',cols = color_pals$class_colors,raster=F,shuffle = T)
p4 <- DimPlot(global.connectomics$CellToCell$RNA,group.by = 'class.Receiving',cols = color_pals$class_colors,raster=F,shuffle = T)
p5 <- DimPlot(global.connectomics$CellToCell$RNA,group.by = 'SendingType',cols = color_pals$type_colors,raster=F,shuffle = T)
p6 <- DimPlot(global.connectomics$CellToCell$RNA,group.by = 'ReceivingType',cols = color_pals$type_colors,raster=F,shuffle = T)
pdf(file='global.connectomics$CellToCell$RNA.UMAPS.pdf',width=20,height=10)
print(plot_grid(p1,p2,p3,p4,p5,p6))
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,p3,p4,p5,p6)
p1 <- DimPlot(global.connectomics$CellToCell$alra,group.by = 'Dataset2.Sending',cols=color_pals$dataset_colors,raster=F,shuffle = T)
p2 <- DimPlot(global.connectomics$CellToCell$alra,group.by = 'orig.ident',cols = color_pals$sample_colors,raster=F,shuffle = T)
p3 <- DimPlot(global.connectomics$CellToCell$alra,group.by = 'class.Sending',cols = color_pals$class_colors,raster=F,shuffle = T)
p4 <- DimPlot(global.connectomics$CellToCell$alra,group.by = 'class.Receiving',cols = color_pals$class_colors,raster=F,shuffle = T)
p5 <- DimPlot(global.connectomics$CellToCell$alra,group.by = 'SendingType',cols = color_pals$type_colors,raster=F,shuffle = T)
p6 <- DimPlot(global.connectomics$CellToCell$alra,group.by = 'ReceivingType',cols = color_pals$type_colors,raster=F,shuffle = T)
pdf(file='global.connectomics$CellToCell$alra.UMAPS.pdf',width=20,height=10)
print(plot_grid(p1,p2,p3,p4,p5,p6))
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,p3,p4,p5,p6)
p1 <- DimPlot(global.connectomics$SystemToCell$RNA,group.by = 'Dataset2',cols=color_pals$dataset_colors,raster=F,shuffle = T)
p2 <- DimPlot(global.connectomics$SystemToCell$RNA,group.by = 'orig.ident',cols = color_pals$sample_colors,raster=F,shuffle = T)
p3 <- DimPlot(global.connectomics$SystemToCell$RNA,group.by = 'class',cols = color_pals$class_colors,raster=F,shuffle = T)
p4 <- DimPlot(global.connectomics$SystemToCell$RNA,group.by = 'ReceivingType',cols = color_pals$type_colors,raster=F,shuffle = T)
pdf(file='global.connectomics$SystemToCell$RNA.UMAPS.pdf',width=10,height=8)
print(plot_grid(p1,p2,p3,p4))
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,p3,p4)
p1 <- DimPlot(global.connectomics$SystemToCell$alra,group.by = 'Dataset2',cols=color_pals$dataset_colors,raster=F,shuffle = T)
p2 <- DimPlot(global.connectomics$SystemToCell$alra,group.by = 'orig.ident',cols = color_pals$sample_colors,raster=F,shuffle = T)
p3 <- DimPlot(global.connectomics$SystemToCell$alra,group.by = 'class',cols = color_pals$class_colors,raster=F,shuffle = T)
p4 <- DimPlot(global.connectomics$SystemToCell$alra,group.by = 'ReceivingType',cols = color_pals$type_colors,raster=F,shuffle = T)
pdf(file='global.connectomics$SystemToCell$alra.UMAPS.pdf',width=10,height=8)
print(plot_grid(p1,p2,p3,p4))
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,p3,p4)
p1 <- DimPlot(global.connectomics$CellToSystem$RNA,group.by = 'Dataset2',cols=color_pals$dataset_colors,raster=F,shuffle = T)
p2 <- DimPlot(global.connectomics$CellToSystem$RNA,group.by = 'orig.ident',cols = color_pals$sample_colors,raster=F,shuffle = T)
p3 <- DimPlot(global.connectomics$CellToSystem$RNA,group.by = 'class',cols = color_pals$class_colors,raster=F,shuffle = T)
p4 <- DimPlot(global.connectomics$CellToSystem$RNA,group.by = 'SendingType',cols = color_pals$type_colors,raster=F,shuffle = T)
pdf(file='global.connectomics$CellToSystem$RNA.UMAPS.pdf',width=10,height=8)
print(plot_grid(p1,p2,p3,p4))
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,p3,p4)
p1 <- DimPlot(global.connectomics$CellToSystem$alra,group.by = 'Dataset2',cols=color_pals$dataset_colors,raster=F,shuffle = T)
p2 <- DimPlot(global.connectomics$CellToSystem$alra,group.by = 'orig.ident',cols = color_pals$sample_colors,raster=F,shuffle = T)
p3 <- DimPlot(global.connectomics$CellToSystem$alra,group.by = 'class',cols = color_pals$class_colors,raster=F,shuffle = T)
p4 <- DimPlot(global.connectomics$CellToSystem$alra,group.by = 'SendingType',cols = color_pals$type_colors,raster=F,shuffle = T)
pdf(file='global.connectomics$CellToSystem$alra.UMAPS.pdf',width=10,height=8)
print(plot_grid(p1,p2,p3,p4))
dev.off()
## quartz_off_screen
## 2
plot_grid(p1,p2,p3,p4)